A state-space approach to modelling brain dynamics

Moon Ho Ringo Ho*, Hernando Ombao, Robert Shumway

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

We propose a state-space approach for studying the dynamic relationship between multiple brain regions. Our approach decomposes the observed multiple time series into measurement error and the BOLD (blood oxygenation level dependent) signals. The proposed model consists of the activation and connectivity equations. In the activation equation, we model the observed signals at each brain region as a function of the BOLD signal. One special feature of our model for capturing the complexities of the dynamic processes in the brain is that the region-specific time-varying coefficients in the activation equation are subsequently modelled, in the connectivity equation, as a function of the BOLD signals at other brain regions. Because our model has a state-space representation, the parameters are readily estimated by maximum likelihood via a routine application of the Kalman filter and smoother. In this paper, we apply our model to a functional magnetic resonance imaging data set to investigate the attentional control network in the brain.

Original languageEnglish (US)
Pages (from-to)407-425
Number of pages19
JournalStatistica Sinica
Volume15
Issue number2
StatePublished - Apr 1 2005

Keywords

  • Effective connectivity
  • Functional magnetic resonance imaging
  • Kalman filter
  • State-space model

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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